Three categories of AI agent safety tooling: observability, security guardrails, and compliance evidence. What each does, where each falls short, and the one most teams are missing.
Bottom line: tools for keeping AI agents safe fall into three groups. Observability tells you what an agent did after the fact. Security guardrails try to block dangerous actions before they happen. Compliance evidence tools produce a verifiable, defensible record that an agent's actions were allowed. Most teams deploying agents into regulated or high-stakes work need all three, but the one almost nobody has is the third. If you have to prove to a regulator, an auditor, or a customer that your agent behaved, you need evidence, not a dashboard.
This is a practitioner comparison, written by the founder of one of the tools below. It names where each category is strong and where it falls short, including our own limits.
1. Observability and evals
These tools capture traces of what your agent did and let you evaluate quality. They are essential for debugging and improving agents, and the category is mature and well funded.
